| description abstract | Wind energy is a key renewable source, yet wind farms have relatively high cost compared with many traditional energy sources. Among the life cycle costs of wind farms, operation and maintenance (O&M) accounts for 25–30%, and an efficient strategy for management of turbines can significantly reduce the O&M cost. Wind turbines are subject to fatigue-induced degradation and need periodic inspections and repairs, which are usually performed through semiannual scheduled maintenance. However, better maintenance can be achieved by flexible policies based on prior knowledge of the degradation process and on data collected in the field by sensors and visual inspections. Traditional methods to model the O&M process, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), have limitations that do not allow the model to properly include the knowledge available and that may result in nonoptimal strategies for management of the farm. Specifically, the conditional probabilities for modeling the degradation process and the precision of the observations are usually affected by epistemic uncertainty. Although MDPs and POMDPs are formulated for fixed transition and emission probabilities, the Bayes-adaptive POMDP (BA-POMDP) framework treats those conditional probabilities as random variables and is therefore suitable for including epistemic uncertainty. In this paper, a novel learning and planning method is proposed, called planning and learning in uncertain dynamic systems (PLUS), within the BA-POMDP framework that can learn from the environment, update the distributions of model parameters, and select the optimal strategy considering the uncertainty related to the model. Validating with synthetic data, the total management cost of a wind farm using PLUS is shown to be significantly less than costs achieved by a fixed policy or through the POMDP framework. The preliminary results show the promise of the proposed methodology for optimal management of wind farms. | |
